Abstract
Network representation learning called NRL for short aims at embedding various networks into low-dimensional continuous distributed vector spaces. Most existing representation learning methods focus on learning representations purely based on the network topology, i.e., the linkage relationships between network nodes, but the nodes in lots of networks may contain rich text features, which are beneficial to network analysis tasks, such as node classification, link prediction and so on. In this paper, we propose a novel network representation learning model, which is named as Text-Enhanced Network Representation Learning called TENR for short, by introducing text features of the nodes to learn more discriminative network representations, which come from joint learning of both the network topology and text features, and include common influencing factors of both parties. In the experiments, we evaluate our proposed method and other baseline methods on the task of node classification. The experimental results demonstrate that our method outperforms other baseline methods on three real-world datasets.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (Grant Nos. 11661069 and 61763041), the Program for Changjiang Scholars and Innovative Research Team in Universities (IRT_15R40).
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Yu Zhu received his MS from Chang’an University, China in 2012. He is a PhD student in Qinghai Normal University as well as a teacher in Qinghai University at present. His research interests include data mining and machine learning.
Zhonglin Ye received his MS from Southwest Jiaotong University, China in 2016. He received his Doctor of Engineering Degree from Shaanxi Normal University, China in 2019. He is a teacher in Qinghai Normal University at present. His research interests include data mining and machine learning.
Haixing Zhao received his Doctor of Engineering Degree from School of Computer Science in Northwestern Polytechnical University, China in 2004, and also received his Doctor of Science Degree from University of Twente, Holland in 2005. He is a professor and part-time professor in Qinghai Normal University and Shaanxi Normal University respectively. His research interests include complex networks and applications, machine translation and machine learning.
Ke Zhang received his MS from Qinghai Normal University, China in 2014, and received his Doctor of Engineering Degree from Qinghai Normal University, China in 2019. He is a teacher in Huzhou University at present. His research interests include hyper-graph theory and complex network.
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Zhu, Y., Ye, Z., Zhao, H. et al. Text-enhanced network representation learning. Front. Comput. Sci. 14, 146322 (2020). https://doi.org/10.1007/s11704-020-8440-6
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DOI: https://doi.org/10.1007/s11704-020-8440-6